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Future Internet
Volume 17
Issue 6
10.3390/fi17060264
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

An Explainable Machine Learning Approach for IoT-Supported Shaft Power Estimation and Performance Analysis for Marine Vessels

by
Yiannis Kiouvrekis
1,2,3,*,
Katerina Gkirtzou
4,
Sotiris Zikas
1,
Dimitris Kalatzis
1,
Theodor Panagiotakopoulos
5,
Zoran Lajic
6,
Dimitris Papathanasiou
6 and
Ioannis Filippopoulos
7
1
Mathematics, Computer Science and Artificial Intelligence Lab, Faculty of Public and One Health, University of Thessaly, 43100 Karditsa, Greece
2
Department of Information Technologies, University of Limassol, 3020 Limassol, Cyprus
3
Business School, University of Nicosia, 2417 Nicosia, Cyprus
4
Institute for Language and Speech Processing, Athena Research Center, 15125 Athens, Greece
5
Department of Management Science and Technology, University of Patras, 26334 Patras, Greece
6
Angelicoussis Group, 17674 Athens, Greece
7
Shipping Operations and Computer Science, University of Limassol, 3086 Limassol, Cyprus
*
Author to whom correspondence should be addressed.
Future Internet 2025, 17(6), 264; https://doi.org/10.3390/fi17060264
Submission received: 30 April 2025 / Revised: 2 June 2025 / Accepted: 12 June 2025 / Published: 17 June 2025
(This article belongs to the Special Issue IoT Architecture for Smart Environments: Mechanisms, Approaches, and Applications)
Versions Notes

Abstract

In the evolving landscape of green shipping, the accurate estimation of shaft power is critical for reducing fuel consumption and greenhouse gas emissions. This study presents an explainable machine learning framework for shaft power prediction, utilising real-world Internet of Things (IoT) sensor data collected from nine (9) Very Large Crude Carriers (VLCCs) over a 36-month period. A diverse set of models—ranging from traditional algorithms such as Decision Trees and Support Vector Machines to advanced ensemble methods like XGBoost and LightGBM—were developed and evaluated. Model performance was assessed using the coefficient of determination (R2) and RMSE, with XGBoost achieving the highest accuracy (R2=0.9490, RMSE 888) and LightGBM being close behind (R2=0.9474, RMSE 902), with both substantially exceeding the industry baseline model (R2=0.9028, RMSE 1500). Explainability was integrated through SHapley Additive exPlanations (SHAP), offering detailed insights into the influence of each input variable. Features such as draft, GPS speed, and time since last dry dock consistently emerged as key predictors. The results demonstrate the robustness and interpretability of tree-based methods, offering a data-driven alternative to traditional performance estimation techniques and supporting the maritime industry’s transition toward more efficient and sustainable operations.
Keywords: explainable artificial intelligence; shaft power estimation; machine learning; vessel performance monitoring; maritime data analytics; fuel efficiency explainable artificial intelligence; shaft power estimation; machine learning; vessel performance monitoring; maritime data analytics; fuel efficiency

Share and Cite

MDPI and ACS Style

Kiouvrekis, Y.; Gkirtzou, K.; Zikas, S.; Kalatzis, D.; Panagiotakopoulos, T.; Lajic, Z.; Papathanasiou, D.; Filippopoulos, I. An Explainable Machine Learning Approach for IoT-Supported Shaft Power Estimation and Performance Analysis for Marine Vessels. Future Internet 2025, 17, 264. https://doi.org/10.3390/fi17060264

AMA Style

Kiouvrekis Y, Gkirtzou K, Zikas S, Kalatzis D, Panagiotakopoulos T, Lajic Z, Papathanasiou D, Filippopoulos I. An Explainable Machine Learning Approach for IoT-Supported Shaft Power Estimation and Performance Analysis for Marine Vessels. Future Internet. 2025; 17(6):264. https://doi.org/10.3390/fi17060264

Chicago/Turabian Style

Kiouvrekis, Yiannis, Katerina Gkirtzou, Sotiris Zikas, Dimitris Kalatzis, Theodor Panagiotakopoulos, Zoran Lajic, Dimitris Papathanasiou, and Ioannis Filippopoulos. 2025. "An Explainable Machine Learning Approach for IoT-Supported Shaft Power Estimation and Performance Analysis for Marine Vessels" Future Internet 17, no. 6: 264. https://doi.org/10.3390/fi17060264

APA Style

Kiouvrekis, Y., Gkirtzou, K., Zikas, S., Kalatzis, D., Panagiotakopoulos, T., Lajic, Z., Papathanasiou, D., & Filippopoulos, I. (2025). An Explainable Machine Learning Approach for IoT-Supported Shaft Power Estimation and Performance Analysis for Marine Vessels. Future Internet, 17(6), 264. https://doi.org/10.3390/fi17060264

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